Warning: This dashboard contains the results of a predictive model that was not built by an epidemiologist.

Note: Click a country name to open a search results page for that country’s COVID-19 news.

Based on data up to: 2020-08-07

World map (interactive)

Includes only countries with at least 1000 reported cases or at least 20 reported deaths.

Tip: Select columns to show on map to from the dropdown menus. The map is zoomable and draggable.

Tables

Projected need for ICU beds

Countries sorted by current estimated need, split into Growing and Recovering countries by current transmission rate. Only for countries with ICU need higher than 0.1 beds per 100k. More details in Appendix.

Growing countries (transmission rate above 5%)

Estimated
current
ICU need
per 100k
population		 Estimated
daily
transmission
rate		 Projected
ICU need
per 100k
In 14 days		 Projected
ICU need
per 100k
In 30 days		 Pre-COVID
ICU
capacity
per 100k
🇵🇪 Peru 5.23 7.0% ± 1.4% 6.0 ± 1.0 6.8 ± 2.2 -
🇧🇷 Brazil 4.66 5.6% ± 1.7% 4.7 ± 1.0 4.7 ± 2.2 -
🇨🇴 Colombia 4.11 6.4% ± 1.0% 4.7 ± 0.6 5.4 ± 1.4 -
🇧🇴 Bolivia 3.56 5.4% ± 0.7% 3.6 ± 0.3 3.5 ± 0.7 -
🇵🇦 Panama 3.55 5.1% ± 0.6% 3.4 ± 0.3 3.2 ± 0.6 -
🇧🇪 Belgium 3.14 7.4% ± 2.4% 4.0 ± 1.2 noisy data 15.9
🇪🇸 Spain 3.09 8.9% ± 2.4% 4.8 ± 1.7 noisy data 9.7
🇪🇨 Ecuador 2.24 5.9% ± 2.4% 2.4 ± 0.7 noisy data -
🇮🇶 Iraq 1.68 5.9% ± 0.4% 1.8 ± 0.1 1.9 ± 0.2 -
🇷🇴 Romania 1.66 6.0% ± 0.9% 1.9 ± 0.2 2.2 ± 0.5 -
🇦🇷 Argentina 1.63 6.7% ± 0.8% 2.0 ± 0.2 2.6 ± 0.6 -
🇧🇦 Bosnia 1.61 5.3% ± 1.4% 1.7 ± 0.3 1.7 ± 0.7 -
🇲🇩 Moldova 1.57 5.3% ± 1.7% 1.6 ± 0.4 1.6 ± 0.8 -
🇫🇷 France 1.39 noisy data noisy data noisy data 11.6
🇳🇱 Netherlands 1.21 9.3% ± 2.0% 2.1 ± 0.7 noisy data 6.4
🇮🇷 Iran 1.12 5.0% ± 0.2% 1.1 ± 0.0 1.0 ± 0.1 4.6
🇸🇻 El Salvador 1.10 5.6% ± 0.2% 1.2 ± 0.0 1.3 ± 0.1 -
🇬🇧 United Kingdom 1.07 5.6% ± 0.7% 1.1 ± 0.1 1.1 ± 0.2 6.6
🇸🇷 Suriname 1.04 6.8% ± 2.2% 1.3 ± 0.4 noisy data -
🇮🇱 Israel 0.86 5.4% ± 0.5% 0.9 ± 0.1 1.0 ± 0.1 -
🇦🇱 Albania 0.79 6.1% ± 0.5% 0.9 ± 0.1 1.1 ± 0.2 -
🇨🇷 Costa Rica 0.78 6.1% ± 1.8% 0.9 ± 0.2 noisy data -
🇧🇬 Bulgaria 0.74 5.6% ± 1.0% 0.8 ± 0.1 0.9 ± 0.3 -
🇨🇻 Cabo Verde 0.55 5.6% ± 0.8% 0.6 ± 0.1 0.6 ± 0.2 -
🇨🇭 Switzerland 0.52 5.2% ± 1.5% 0.5 ± 0.1 0.5 ± 0.2 11.0
🇲🇻 Maldives 0.52 7.2% ± 1.9% 0.7 ± 0.2 noisy data -
🇮🇳 India 0.51 6.3% ± 0.3% 0.6 ± 0.0 0.7 ± 0.1 5.2
🇬🇲 Gambia 0.49 16.7% ± 5.7% noisy data noisy data -
🇬🇾 Guyana 0.44 noisy data noisy data noisy data -
🇱🇾 Libya 0.40 9.5% ± 3.0% noisy data noisy data -
🇮🇹 Italy 0.40 5.8% ± 2.5% 0.4 ± 0.1 noisy data 12.5
🇺🇦 Ukraine 0.38 6.0% ± 0.7% 0.4 ± 0.0 0.5 ± 0.1 -
🇵🇭 Philippines 0.36 7.4% ± 2.5% 0.5 ± 0.2 noisy data 2.2
🇵🇱 Poland 0.32 6.6% ± 0.6% 0.4 ± 0.0 0.5 ± 0.1 6.9
🇮🇪 Ireland 0.31 8.3% ± 2.7% 0.5 ± 0.2 noisy data 6.5
🇩🇰 Denmark 0.31 8.7% ± 2.6% 0.5 ± 0.2 noisy data 6.7
🇸🇬 Singapore 0.29 noisy data noisy data noisy data 11.4
🇵🇸 West Bank and Gaza 0.27 5.1% ± 0.6% 0.3 ± 0.0 0.3 ± 0.0 -
🇨🇿 Czechia 0.26 6.4% ± 1.1% 0.3 ± 0.0 0.4 ± 0.1 11.6
🇿🇲 Zambia 0.22 6.0% ± 1.4% 0.2 ± 0.0 0.3 ± 0.1 -
🇩🇪 Germany 0.21 7.1% ± 1.4% 0.3 ± 0.1 0.4 ± 0.2 29.2
🇮🇩 Indonesia 0.20 5.4% ± 0.6% 0.2 ± 0.0 0.2 ± 0.0 2.7
🇲🇦 Morocco 0.20 8.5% ± 1.6% 0.3 ± 0.1 noisy data -
🇱🇧 Lebanon 0.20 7.9% ± 1.3% 0.3 ± 0.1 0.5 ± 0.2 -
🇹🇷 Turkey 0.19 5.0% ± 0.3% 0.2 ± 0.0 0.2 ± 0.0 47.1
🇬🇷 Greece 0.18 9.8% ± 1.9% 0.4 ± 0.1 noisy data 6.0
🇵🇾 Paraguay 0.16 6.4% ± 2.9% 0.2 ± 0.1 noisy data -
🇦🇺 Australia 0.15 5.9% ± 1.6% 0.2 ± 0.0 noisy data 9.1
🇩🇯 Djibouti 0.15 noisy data noisy data noisy data -
🇳🇦 Namibia 0.15 7.1% ± 2.0% 0.2 ± 0.1 noisy data -
🇻🇪 Venezuela 0.14 7.1% ± 1.1% 0.2 ± 0.0 0.3 ± 0.1 -
🇯🇵 Japan 0.13 7.3% ± 0.9% 0.2 ± 0.0 0.3 ± 0.1 7.3
🇰🇪 Kenya 0.13 5.5% ± 0.6% 0.1 ± 0.0 0.1 ± 0.0 -
🇭🇺 Hungary 0.12 noisy data 0.1 ± 0.1 noisy data 13.8
🇱🇹 Lithuania 0.11 6.4% ± 2.3% 0.1 ± 0.0 noisy data 15.5

Recovering countries (tranmission rate below 5%)

Estimated
current
ICU need
per 100k
population		 Estimated
daily
transmission
rate		 Projected
ICU need
per 100k
In 14 days		 Projected
ICU need
per 100k
In 30 days		 Pre-COVID
ICU
capacity
per 100k
🇺🇸 US 3.91 4.6% ± 0.4% 3.6 ± 0.2 3.2 ± 0.3 34.7
🇲🇽 Mexico 3.72 4.9% ± 0.5% 3.4 ± 0.2 3.1 ± 0.4 1.2
🇰🇬 Kyrgyzstan 2.99 3.1% ± 0.4% 2.3 ± 0.1 1.6 ± 0.2 -
🇨🇱 Chile 2.08 3.9% ± 0.5% 1.7 ± 0.1 1.4 ± 0.2 -
🇿🇦 South Africa 1.97 3.5% ± 0.8% 1.6 ± 0.2 1.2 ± 0.3 -
🇲🇰 North Macedonia 1.88 3.6% ± 1.1% 1.5 ± 0.2 1.2 ± 0.4 -
🇸🇪 Sweden 1.48 4.3% ± 1.4% 1.3 ± 0.2 1.1 ± 0.5 5.8
🇭🇳 Honduras 1.45 4.7% ± 0.7% 1.4 ± 0.1 1.2 ± 0.2 -
🇲🇪 Montenegro 1.41 3.9% ± 0.6% 1.2 ± 0.1 1.0 ± 0.2 -
🇬🇹 Guatemala 1.37 4.3% ± 1.6% 1.2 ± 0.3 1.0 ± 0.5 -
🇦🇲 Armenia 1.35 2.7% ± 1.1% 1.0 ± 0.2 0.7 ± 0.2 -
🇱🇺 Luxembourg 1.29 noisy data 1.0 ± 0.2 noisy data 24.8
🇩🇴 Dominican Republic 1.25 4.3% ± 0.7% 1.1 ± 0.1 1.0 ± 0.2 -
Eswatini 0.69 4.2% ± 1.4% 0.6 ± 0.1 0.5 ± 0.2 -
🇰🇼 Kuwait 0.63 4.4% ± 0.8% 0.6 ± 0.1 0.5 ± 0.1 -
🇴🇲 Oman 0.62 1.5% ± 0.6% 0.4 ± 0.0 0.2 ± 0.0 14.6
🇷🇸 Serbia 0.57 4.0% ± 0.2% 0.5 ± 0.0 0.4 ± 0.0 -
🇧🇭 Bahrain 0.57 4.2% ± 0.5% 0.5 ± 0.0 0.4 ± 0.1 -
🇰🇿 Kazakhstan 0.55 3.4% ± 0.3% 0.4 ± 0.0 0.3 ± 0.0 21.3
🇨🇦 Canada 0.53 4.7% ± 1.8% 0.5 ± 0.1 noisy data 13.5
🇵🇹 Portugal 0.48 3.5% ± 1.3% 0.4 ± 0.1 0.3 ± 0.1 4.2
🇷🇺 Russia 0.43 4.1% ± 0.1% 0.4 ± 0.0 0.3 ± 0.0 8.3
🇸🇦 Saudi Arabia 0.42 3.0% ± 0.2% 0.3 ± 0.0 0.2 ± 0.0 22.8
🇦🇿 Azerbaijan 0.32 2.4% ± 0.6% 0.2 ± 0.0 0.2 ± 0.0 -
🇩🇿 Algeria 0.32 4.8% ± 0.2% 0.3 ± 0.0 0.3 ± 0.0 -
🇬🇶 Equatorial Guinea 0.32 noisy data 0.2 ± 0.0 0.1 ± 0.0 -
🇭🇷 Croatia 0.28 3.1% ± 1.2% 0.2 ± 0.0 0.2 ± 0.1 -
🇦🇹 Austria 0.25 4.6% ± 1.0% 0.2 ± 0.0 0.2 ± 0.1 21.8
🇸🇮 Slovenia 0.25 noisy data 0.2 ± 0.1 noisy data 6.4
🇶🇦 Qatar 0.22 3.0% ± 0.4% 0.2 ± 0.0 0.1 ± 0.0 -
🇱🇸 Lesotho 0.21 noisy data 0.1 ± 0.0 0.1 ± 0.0 -
🇪🇬 Egypt 0.17 1.1% ± 0.1% 0.1 ± 0.0 0.1 ± 0.0 -
🇿🇼 Zimbabwe 0.17 4.1% ± 1.7% 0.1 ± 0.0 noisy data -
🇲🇷 Mauritania 0.16 noisy data 0.1 ± 0.0 noisy data -
🇬🇦 Gabon 0.14 4.3% ± 0.3% 0.1 ± 0.0 0.1 ± 0.0 -
🇧🇩 Bangladesh 0.14 4.1% ± 1.0% 0.1 ± 0.0 0.1 ± 0.0 0.7
🇦🇪 UAE 0.11 3.2% ± 0.5% 0.1 ± 0.0 0.1 ± 0.0 -
🇧🇾 Belarus 0.11 2.8% ± 0.4% 0.1 ± 0.0 0.1 ± 0.0 -
🇳🇮 Nicaragua 0.11 noisy data 0.1 ± 0.0 noisy data -

Appendix

Interactive plot of model predictions and past data

Tip: Choose a country from the drop-down menu to see the calculations used in the tables above and the dynamics of the model.

Projected Affected Population percentages

Top 20 countries with most estimated recent cases. Sorted by number of estimated recent cases during the last 5 days. More details in Appendix.

Estimated
recent cases
during
last 5 days		 Estimated
total
affected
population
percentage		 Estimated
daily
transmission
rate		 Projected
total
affected
percentage
In 14 days		 Projected
total
affected
percentage
In 30 days		 Lagged
fatality
rate
🇮🇳 India 1,288,083 0.9% 6.3% ± 0.3% 1.2% ± 0.0% 1.7% ± 0.1% 2.5%
🇧🇷 Brazil 1,251,805 8.4% 5.6% ± 1.7% 10.1% ± 0.8% 12.2% ± 2.1% 3.7%
🇺🇸 US 820,340 5.4% 4.6% ± 0.4% 6.2% ± 0.1% 7.0% ± 0.2% 3.5%
🇲🇽 Mexico 586,118 8.4% 4.9% ± 0.5% 9.8% ± 0.2% 11.4% ± 0.5% 12.1%
🇨🇴 Colombia 272,276 5.1% 6.4% ± 1.0% 7.2% ± 0.5% 10.0% ± 1.4% 4.1%
🇵🇪 Peru 268,408 12.5% 7.0% ± 1.4% 15.2% ± 0.8% 18.8% ± 2.3% 5.1%
🇮🇶 Iraq 189,791 5.7% 5.9% ± 0.4% 7.4% ± 0.1% 9.5% ± 0.4% 4.2%
🇿🇦 South Africa 145,661 4.7% 3.5% ± 0.8% 5.4% ± 0.2% 6.1% ± 0.5% 2.0%
🇪🇸 Spain 136,409 5.4% 8.9% ± 2.4% 6.6% ± 0.6% 8.9% ± 2.6% 9.9%
🇮🇷 Iran 133,845 4.9% 5.0% ± 0.2% 5.4% ± 0.0% 6.1% ± 0.1% 6.0%
🇫🇷 France 110,867 4.0% noisy data 4.6% ± 0.9% noisy data 13.5%
🇵🇭 Philippines 96,498 0.7% 7.4% ± 2.5% 1.0% ± 0.2% 1.7% ± 0.8% 2.3%
🇦🇷 Argentina 94,150 1.8% 6.7% ± 0.8% 2.5% ± 0.1% 3.7% ± 0.5% 2.3%
🇮🇩 Indonesia 90,705 0.5% 5.4% ± 0.6% 0.6% ± 0.0% 0.8% ± 0.1% 5.2%
🇪🇨 Ecuador 66,035 7.1% 5.9% ± 2.4% 8.2% ± 0.7% 9.7% ± 2.0% 6.9%
🇧🇴 Bolivia 55,558 7.0% 5.4% ± 0.7% 8.7% ± 0.3% 10.7% ± 0.8% 4.6%
🇬🇧 United Kingdom 46,992 6.3% 5.6% ± 0.7% 6.5% ± 0.0% 6.8% ± 0.1% 15.3%
🇬🇹 Guatemala 45,525 3.8% 4.3% ± 1.6% 4.5% ± 0.4% 5.3% ± 0.9% 4.4%
🇷🇺 Russia 39,149 1.1% 4.1% ± 0.1% 1.2% ± 0.0% 1.3% ± 0.0% 1.8%
🇧🇩 Bangladesh 35,352 0.5% 4.1% ± 1.0% 0.6% ± 0.0% 0.7% ± 0.1% 1.4%

Methodology

  • I'm not an epidemiologist. This is an attempt to understand what's happening, and what the future looks like if current trends remain unchanged.
  • Everything is approximated and depends heavily on underlying assumptions.
  • Projection is done using a simple SIR model (see examples) combined with the approach in Total Outstanding Cases:
    • Growth rate is calculated over the 5 past days by averaging the daily growth rates.
    • Confidence bounds are calculated by from the weighted standard deviation of the growth rate over the last 5 days. Model predictions are calculated for growth rates within 1 STD of the weighted mean. The maximum and minimum values for each day are used as confidence bands.
    • Transmission rate, and its STD are calculated from growth rate and its STD using active cases estimation mentioned above.
    • For projections (into future) very noisy projections (with broad confidence bounds) are not shown in the tables.
    • Where the rate estimated from Total Outstanding Cases is too high (on down-slopes) recovery probability if 1/20 is used (equivalent 20 days to recover).
  • Total cases are estimated from the reported deaths for each country:
    • Each country has a different testing policy and capacity and cases are under-reported in some countries. Using an estimated IFR (fatality rate) we can estimate the number of cases some time ago by using the total deaths until today.
    • IFRs for each country is estimated using the age adjusted IFRs from May 1 New York paper and UN demographic data for 2020. These IFRs can be found in df['age_adjusted_ifr'] column. Some examples: US - 0.98%, UK - 1.1%, Qatar - 0.25%, Italy - 1.4%, Japan - 1.6%.
    • The average fatality lag is assumed to be 8 days on average for a case to go from being confirmed positive (after incubation + testing lag) to death. This is the same figure used by "Estimating The Infected Population From Deaths".
    • Testing bias adjustment: the actual lagged fatality rate is than divided by the IFR to estimate the testing bias in a country. The estimated testing bias then multiplies the reported case numbers to estimate the true case numbers (=case numbers if testing coverage was as comprehensive as in the heavily tested countries).
  • ICU need is calculated and age-adjusted as follows:
    • UK ICU ratio was reported as 4.4% of active reported cases.
    • Using UKs ICU ratio, UK's testing bias, and IFRs corrected for age demographics we can estimate each country's ICU ratio (the number of cases requiring ICU hospitalisation).
    • Active cases for ICU estimation are taken from the SIR model.
    • Pre COVID-19 ICU capacities are from Wikipedia (OECD countries mostly) and CCB capacities in Asia. The current capacities are likely much higher as some countries already doubled or even quadrupled their ICU capacities.